class_docstr: Multi-node Multi-GPU K-Nearest Neighbors Regressor Model.
  
  K-Nearest Neighbors Regressor is an instance-based learning technique,
  that keeps training samples around for prediction, rather than trying
  to learn a generalizable set of model parameters.
  
  Parameters
  ----------
  n_neighbors : int (default=5)
      Default number of neighbors to query
  batch_size: int (optional, default 2000000)
      Maximum number of query rows processed at once. This parameter can
      greatly affect the throughput of the algorithm. The optimal setting
      of this value will vary for different layouts and index to query
      ratios, but it will require `batch_size * n_features * 4` bytes of
      additional memory on each worker hosting index partitions.
  handle : cuml.Handle
      Specifies the cuml.handle that holds internal CUDA state for
      computations in this model. Most importantly, this specifies the CUDA
      stream that will be used for the model's computations, so users can
      run different models concurrently in different streams by creating
      handles in several streams.
      If it is None, a new one is created.
  verbose : int or boolean, default=False
      Sets logging level. It must be one of `cuml.common.logger.level_*`.
      See :ref:`verbosity-levels` for more info.
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code: def _func_create_model(sessionId, **kwargs):
      try:
          from cuml.neighbors.kneighbors_regressor_mg import (
              KNeighborsRegressorMG as cumlKNN,
          )
      except ImportError:
          raise_mg_import_exception()
  
      handle = get_raft_comm_state(sessionId, get_worker())["handle"]
      return cumlKNN(handle=handle, **kwargs)
  
file_path: cuml/python/cuml/dask/neighbors/kneighbors_regressor.py
imports: from cuml.dask.common.input_utils import DistributedDataHandler
  from cuml.dask.common.input_utils import to_output
  from cuml.dask.common import parts_to_ranks
  from cuml.dask.common import flatten_grouped_results
  from cuml.dask.common.utils import raise_mg_import_exception
  from cuml.dask.common.utils import wait_and_raise_from_futures
  from raft_dask.common.comms import get_raft_comm_state
  from cuml.dask.neighbors import NearestNeighbors
  from dask.distributed import get_worker
  import dask.array
  from uuid import uuid1
label_desc: Use image node_id 3 for calling the KNeighborsRegressor obj's underlying member method code with example usage: obj._func_create_model(sessionId) and returns: cumlKNN